Method and system for assessing mental state

ABSTRACT

A computer-implemented method of assessing a mental state of a subject ( 106 ) includes receiving ( 302 ), as input, a heartbeat record ( 200 ) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period ( 208 ), a sleep period ( 209 ) having a sleep onset time ( 224 ) and a sleep conclusion time ( 226 ), and a post-sleep period ( 210 ). At least the sleep onset time and the sleep conclusion time are identified ( 304 ) within the heartbeat record. A knowledge base ( 124 ) is then accessed ( 306 ), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between mental state and heart rate characteristics. Using information in the knowledge base, the computational model is applied ( 308 ) to compute at least one metric associated with the mental state of the subject, and to generate an indication of mental state based upon the metric. The indication of mental state is provided ( 310 ) as output.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of PCT International Application No.PCT/AU2016/050490 filed on Jun. 15, 2016, which claims priority under 35U.S.C. § 119(e) to U.S. Provisional Application No. 62/175,796 filed onJun. 15, 2015, all of which are hereby expressly incorporated byreference into the present application.

FIELD OF THE INVENTION

The present invention relates generally to the field of mental healthcare, and more particularly to methods and systems, along withassociated hardware and software components, for objectively assessingthe state of mental health of an individual. Embodiments of theinvention may usefully assist health care professionals, and others, inidentifying and/or treating individuals who may be suffering from,recovering from, or at risk of, adverse mental health conditions such asdepression.

BACKGROUND TO THE INVENTION

It is estimated that one in four citizens of developed nations willexperience a mental health problem during their lifetime, with up to 10percent of the population experiencing some type of depressive oranxiety-related disorder every year. The global economic cost of mentalillness is measured in trillions of dollars annually.

Presently, there is no accepted and widely recognised objective test formany mental illnesses, such as depression. The diagnostic ‘goldstandard’ in such cases remains clinical/expert assessment and opinion,based upon interviews with the patient along with close friends andfamily, and self-reporting (e.g. through the completion ofquestionnaires), for comparison against clinical symptoms catalogued inthe Diagnostic and Statistical Manual of Mental Disorders (currentlyDSM-5).

However, due to the subjective nature of many aspects of this diagnosticprocess, agreement between clinicians can vary considerably, even forhigh-prevalence disorders such as depression and anxiety.

There is, accordingly, a need for quantitative, objective tests that canbe employed by clinicians when diagnosing psychological disorders, andfor monitoring the progress of patients undergoing treatment. Ideally,such tests should be simple, safe and unobtrusive, so that they can beundertaken without significant impact on the patient's lifestyle orday-to-day routine.

Provision of objective tests for mental health would enable numeroussignificant benefits to be realised. Better objective information couldlead to earlier diagnosis, earlier intervention, and better outcomes forpatients. Ongoing monitoring of patients could provide an objectiveindication of therapeutic effectiveness, enabling treatments to bevaried and optimised based upon patient response. These improvements intreatment and outcomes would result in savings to the health system, andto the community in general.

It has been known for some time that there is a relationship betweencircadian heart rate patterns and psychological state. For example, U.S.Pat. No. 6,245,021 describes the use of recorded 24-hour heart ratepatterns in the diagnosis of psychological disorders includingdepression, anxiety, panic disorder, obsessive compulsive disorder (OCD)and schizophrenia. However, the procedures disclosed in this patentstill require expert (i.e. human) review of circadian heart ratepatterns, by clinicians with the necessary training and experience toidentify features that are commonly associated with the differentdisorders. Patients are required to maintain a daily diary, whichenables the clinician to compare features in the heart rate patternsagainst activity (e.g. exercise) in which the patient may have engaged,so as to avoid misinterpreting these features. Clearly, a system thatrequires 24-hour monitoring, and the keeping of a daily diary, has anoticeable impact upon the patient's lifestyle and day-to-day routine,leading to a greater likelihood of non-compliance with the measurementand monitoring regime.

Accordingly, it would be desirable to develop new and objective methodsand systems to assist in identifying individuals who may be sufferingfrom, or at risk of, adverse mental health conditions such asdepression, and which are able to provide one or more of the benefitsdiscussed above. The present invention has been devised in order toaddress this need.

SUMMARY OF THE INVENTION

In one aspect, the present invention provides a computer-implementedmethod of assessing a mental state of a subject, the method comprising:

receiving, as input, a heartbeat record of the subject, which comprisesa sequence of heartbeat data samples obtained over a time span whichincludes a pre-sleep period, a sleep period having a sleep onset timeand a sleep conclusion time, and a post-sleep period;

identifying, within the heartbeat record, at least the sleep onset timeand the sleep conclusion time;

accessing a knowledge base comprising data obtained via expertevaluation of a training set of subjects and embodying a computationalmodel of a relationship between mental state and heart ratecharacteristics;

using information in the knowledge base, applying the computationalmodel to compute at least one metric associated with the mental state ofthe subject, and to generate an indication of mental state based uponthe metric; and

providing, as output, the indication of mental state.

Embodiments of the invention may comprise expert systems in which theknowledge base contains information generated via machine-learningmethodologies. For example, the knowledge base may embody measured heartrate data for a plurality of subjects comprising the training set, alongwith the results of expert assessment of each subject in the trainingset. In such embodiments, the knowledge base captures salientinformation regarding the relationship between the mental state of eachsubject in the training set, and measured heart rate characteristics, ina form such that a corresponding computational model may be employed topredict the expert assessment of subsequent unseen test subjects.

According to embodiments of the invention, the indication of mentalstate comprises an indication of mental health of the subject. Forexample, the indication of mental state may distinguish between anominally normal (i.e. relatively healthy) condition, a nominallydepressed condition, and/or one or more other conditions. The otherconditions may be indeterminate, or may be conditions such as stress oranxiety. In any event, the output indication of mental health may not beregarded as a diagnosis, but may be useful to health carepractitioners—and especially to those practitioners who are notthemselves experts in mental health—in identifying individuals who maybe suffering from, or at risk of, adverse mental health conditions. Suchindividuals may then be referred to an appropriate health careprofessional for further review, tests, diagnosis and/or treatment.

Identifying the sleep onset and conclusion times may involve the use ofauxiliary input data. In some embodiments, for example, the inputheartbeat record may be accompanied by a record of activity of thesubject measured using an activity monitor, such as an accelerometer.

In some embodiments, the knowledge base may comprise a template normalheart rate characteristic which may be obtained, for example, byaveraging scaled and normalised heart rate characteristics of subjectsin the training set who have been assessed by an expert assessor ashaving a normal, relatively healthy, mental state. The knowledge basemay further comprise one or more template heart rate characteristicscorresponding with other, e.g. abnormal or unhealthy, mental stateswhich may be obtained, for example, by averaging scaled and normalisedheart rate characteristics of subjects in the training set who have beenassessed by an expert assessor as having such other mental states. Inparticular, the knowledge base may comprise a template depression heartrate characteristic obtained, for example, by averaging scaled andnormalised heart rate characteristics of subjects in the training setwho have been assessed by an expert assessor as having a depressedmental state.

In alternative embodiments, heart rate characteristics of subjects maybe processed to compute a plurality of associated metrics. In someexamples, four metrics are employed: a mean-awake heart rate; a ratiobetween mean-awake and -asleep heart rates; a slope of heart rate duringthe first half of the sleep period; and a slope of heart rate in thesecond half of the sleep period. As will be appreciated, theseparticular four metrics can be computed by fitting a piecewise linearheart rate characteristic model to the received heartbeat record of asubject.

The knowledge base may comprise one or more data structures resultingfrom the application of machine learning algorithms to the metricscomputed by processing the heart rate characteristics of subjects in thetraining set. Suitable machine learning algorithms include: decisiontree learning; association rule learning; artificial neural networks;inductive logic programming; support vector machines; cluster analysis;Bayesian networks; reinforcement learning; representation learning;similarity learning; sparse dictionary learning; genetic algorithms;and/or other methodologies known to persons skilled in the art ofmachine learning.

In some embodiments, the knowledge base comprises data structuresrepresenting one or more classification trees, obtained by applying adecision tree learning algorithm over the metrics computed from theheart rate characteristics of subjects in the training set. As known topersons skilled in the art of machine learning, a number of decisiontree algorithms are known, which may be suitable for this purpose,including: 103 (Iterative Dichotomiser 3); C4.5; CART (Classificationand Regression Tree); CHAID (Chi-square Automatic Interaction Detector);MARS; and conditional inference trees. A number of existing softwareapplications provide implementations of one or more of the foregoinglearning algorithms, including MATLAB and R.

In an embodiment, a decision tree learning algorithm is applied togenerate two classification tree data structures, which are stored inthe knowledge base. A first classification tree data structureclassifies metrics computed from the heartbeat record of the subjectinto ‘normal’ or ‘not normal’. A second classification tree datastructure classifies the metrics computed from the heartbeat record ofthe subject into ‘depressed’ and ‘not depressed’.

According to an embodiment, the method comprises classifying the subjectas ‘normal’ or ‘not normal’ by executing the first classification treeand, in the event that the subject is classified as ‘not normal’,classifying the subject as ‘depressed’ or ‘not depressed’ by executingthe second classification tree.

In another aspect, the invention provides a computer-implemented systemfor assessing a mental state of a subject, the system comprising:

at least one microprocessor;

at least one non-volatile storage device containing a knowledge basecomprising data obtained via expert evaluation of a training set ofsubjects and embodying a computational model of a relationship betweenmental state and heart rate characteristics;

at least one computer-readable memory device operatively associated withthe microprocessor; and

an input/output interface operatively associated with themicroprocessor,

wherein the memory device contains computer-executable instruction codewhich, when executed via the microprocessor, causes the microprocessorto effect a method comprising steps of:

-   -   receiving, via the input/output interface, a heartbeat record of        the subject, which comprises a sequence of heartbeat data        samples obtained over a timespan which includes a pre-sleep        period, a sleep period having a sleep onset time and a sleep        conclusion time, and a post-sleep period;    -   identifying, within the heartbeat record, at least the sleep        onset time and the sleep conclusion time;    -   using information in the knowledge base, applying the        computational model to compute at least one metric associated        with the mental state of the subject, and to generate an        indication of mental state based upon the metric; and    -   providing, via the input/output interface, the indication of the        mental state of the subject.

The input/output interface may be a network interface providing accessto a wide area network, such as the Internet.

In some embodiments of the invention, the heartbeat record of thesubject may be obtained via a heart rate monitor device worn by thesubject during the timespan including the pre-sleep period, the sleepperiod and the post-sleep period. The heartbeat monitor may comprise awireless interface, such as a Bluetooth interface, for communicationwith a network-connected device, such as a smartphone, a tabletcomputer, a notebook computer, or a desktop computer. Alternatively, oradditionally, the heart rate monitor device may comprise a wiredinterface, such as a USB interface, for connection to anetwork-connected device. A heartbeat record obtained via the heart ratemonitor device may be transferred continuously (i.e. in real time) toanother device. Alternatively, the heartbeat record, or a portionthereof, may be stored within the heart rate monitor device and data maybe transferred periodically, upon completion of recording, or at a latertime, e.g. upon connection to a network or suitable network-connecteddevice.

An application may be provided for execution on the network-connecteddevice to assist the subject in performing a measurement of a heartbeatrecord. Assistance may include providing the subject with instructionsfor fitting the heart rate monitor device, as well as for transferringmeasured heart rate data from the heart rate monitor device to thenetwork-connected device.

The heartbeat record of the subject may be transferred from thenetwork-connected device to the mental state assessment system via thewide area network, e.g. the Internet.

Further features and benefits of the invention will be apparent from thefollowing description of embodiments, which is provided by way ofexample only and should not be taken to limit the scope of the inventionas it is defined in any of the preceding statements, or in the claimsappended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described with reference to theaccompanying drawings in which like reference numerals indicate likefeatures, and wherein:

FIG. 1 is a schematic diagram illustrating a system for assessing mentalstate of a subject, embodying the invention;

FIGS. 2(a) and 2(b) show graphs of exemplary heart rate and activityrecords embodying the invention;

FIG. 3 shows a flowchart of a method of assessing a mental stateembodying the invention;

FIGS. 4(a) and 4(b) are flowcharts corresponding with two alternativecomputational models embodying the invention;

FIGS. 5(a) to 5(c) are block diagrams illustrating the content of aknowledge base corresponding with the computational model of FIG. 4(a);

FIG. 6 is a block diagram illustrating the main software processingcomponents of a computer implementation of embodiments of the invention;

FIG. 7(a) is a flowchart of a knowledge base construction methodcorresponding with the computational model of FIG. 4(a);

FIG. 7(b) is a flowchart of a knowledge base construction methodcorresponding with the computational model of FIG. 4(b);

FIGS. 8(a) and 8(b) show three-dimensional chart representations ofsegmentation of subjects in a training set having an average wakingheart rate of around 80 beats per minute;

FIGS. 9(a) and 9(b) show three-dimensional chart representations ofsegmentation of subjects in a training set having an average awake heartrate of around 87.7 beats per minute;

FIGS. 10(a) and 10(b) show three-dimensional chart representations ofsegmentation of subjects in a training set having an average wakingheart rate of around 96 beats per minute; and

FIG. 11 is a block diagram illustrating a process of evaluation,diagnosis and treatment employing an embodiment of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS

FIG. 1 is a block diagram illustrating an online system 100 embodyingthe invention. The system 100 employs a wide area communications network102, typically being the Internet, for messaging between differentcomponents of the system, each of which generally comprises one or morecomputing devices.

The system 100 includes an assessment platform 104 and an assessmentsubject 106 who is, in this example, located remotely from theassessment platform 104. The subject 106 is provided with a heart ratemonitor 108, which may be capable of communications with one or moreportable devices, such as smartphone 110, and/or one or more desktopdevices such as a personal computer 112. Communications between theheart rate monitor 108 and smartphone 110 are preferably via a wirelesscommunications channel, such as Bluetooth. Other types of communicationschannel suitable for transfer of data between the heart rate monitor 108and devices 110, 112 include Wi-Fi, wired Ethernet, and other forms ofwired connections, such as USB.

In some embodiments, such as those described herein, heart rate datacollected by the heart rate monitor 108 is transferred to another userdevice, such as smartphone 110 or desktop PC 112, and then transferredto the assessment platform 104. However, in other embodiments of theinvention a smart heart rate monitor 108 may include a networkinterface, such as a Wi-Fi interface or a cellular mobile interfaceincluding, e.g., a Nano Sim card, enabling it to connect and transferdata directly to the assessment platform 104 via the Internet 102.Alternatively, the heart rate monitor 108 may be integrated with acloud-based platform, such as a healthcare platform, e.g. PhilipsHealthsuite, or other cloud platform, e.g. Samsung SAMIIO, for upload ofdata to the cloud for retrieval by the assessment platform 104. In stillfurther embodiments, the functionality of the assessment platform 104may be provided at the location of the assessment subject 106, such asvia software made available for installation on the subject PC 112. Inyet another alternative, the assessment platform 104 may be provided atthe location (e.g. surgery or office) of a health care professional whois monitoring the mental health of the subject 106. Other combinationsand variations of the above arrangements are also possible, within thescope of the invention, such as the collection of heart rate data by themonitor 108 for transfer to a portable or desktop device of a healthcare professional, and subsequent submission for processing by aremotely located assessment platform 104. It should therefore beappreciated that the exemplary architecture of the system 100 is not theonly configuration in which the invention may be implemented.

Turning now to the assessment platform 104, it may generally compriseone or more computers, each of which includes at least onemicroprocessor 114. The number of computers and processors 114 willgenerally depend upon the required processing capacity of the system,which in turn depends upon the anticipated workload, i.e. the number ofassessment subjects 106 having access to the platform 104, and thevolumes of data to be processed. In some embodiments, a third-partycloud-computing platform may be employed for the platform 104, therebyenabling the physical hardware resources to be allocated, and changed,dynamically in response to demand. However, for simplicity in theremainder of the description, it is assumed that the exemplaryassessment platform 104 includes a single computer with a singlemicroprocessor 114.

The microprocessor 114 is interfaced to, or otherwise operablyassociated with, a non-volatile memory/storage device 116. Thenon-volatile storage 116 may be a hard disk drive, and/or may include asolid-state non-volatile memory, such as read only memory (ROM), flashmemory, or the like. The microprocessor 114 is also interfaced tovolatile storage 118, such as random access memory (RAM) which containsprogram instructions and transient data relating to the operation of theplatform 104. In a conventional configuration, the storage device 116may contain operating system programs and data, as well as otherexecutable application software necessary to the intended functions ofthe assessment platform 104. In the embodiments shown, the storagedevice 116 also contains program instructions which, when executed bythe processor 114, enable the assessment platform 104 to performoperations relating to the implementation of a mental state assessmentmethod, and more particularly a method of assessing stress levels of thesubject 106, embodying the invention. In operation, instructions anddata held on the storage device 116 are transferred to volatile memory118 for execution on demand.

The microprocessor 114 is also operably associated with a networkinterface 120 in a conventional manner. The network interface 120facilitates access to one or more data communications networks, such asthe Internet 102, employed for communication between the platform 104and subject devices, e.g. 110, 112.

In use, the volatile storage 118 includes a corresponding body 122 ofprogram instructions configured to perform processing and operationsembodying features of the present invention, comprising various steps inthe processes described below with reference to the flowcharts, datastructures, and software architectures illustrated in FIGS. 3 to 8.

Furthermore, in the presently described embodiment, the programinstructions 122 include instructions implementing communications withone or more client applications, such as an application executing on asmartphone 110, desktop PC 112, or other device operated by the subject106 or a supervising health care professional. These communicationsoperations enable heartbeat records of the subject 106, recorded usingthe heart rate monitor 108, to be received for processing by theassessment platform 104.

The program instructions 122 may further include instructions embodyinga web server application. Data stored in the non-volatile 116 andvolatile 118 storage may then include web-based code for presentationand/or execution on subject devices (e.g. HTML or JavaScript)facilitating a web-based interface to the assessment platform. Theweb-based interface may, for example, enable upload of heartbeat recorddata from any device, including smartphone 110 or desktop PC 112, to theassessment platform 104. The web interface may also enable the subject106 and/or their supervising health care professional, via devices 110and/or 112, to access data that has been stored and processed by theassessment platform 104.

The system 100 also includes a knowledge base 124, which containsinformation generated via machine learning methodologies, using dataobtained via expert evaluation of one or more training sets of subjects,and embodying a computational model of a relationship between mentalstate, e.g. subject mental health, and heart rate characteristics.

Various machine-learning methodologies may be employed in differentembodiments of the invention, including: decision tree learning;association rule learning; artificial neural networks; inductive logicprogramming; support vector machines; cluster analysis; Bayesiannetworks; reinforcement learning; representation learning; similaritylearning; sparse dictionary learning; and/or genetic algorithms.

Embodiments described herein, particularly with reference to FIGS. 4 to8, employ techniques including metric learning and decision treelearning. However, these approaches should be regarded as illustrativeonly, and do not exclude the use of other learning techniques andcomputational models from the scope of the invention.

The knowledge base 124 may be contained within the non-volatile storage116, or may be stored in a separate storage device, which may bedirectly connected to the assessment platform 104, or may be remotelylocated. In particular, since the knowledge base 124 may ultimately growto contain very large amounts of training and historical subject data,it may be advantageous for the knowledge base 124 to be stored in alarge data centre and/or one or more distributed databases, e.g. in acloud storage service. The exact form and location of the knowledge base124 is not critical, so long as the required data, as described below,is accessible for processing by the assessment platform 104.

Turning now to FIG. 2(a), there is shown a graph 200 of an exemplaryheartbeat record of a subject 106. Time is shown on the horizontal axis202, and minute-averaged heart rate in beats per minute, on the verticalaxis 204. Accordingly, the heartbeat record of the subject representedby the graph 200 comprises a sequence of heartbeat data samples,obtained and recorded at a rate of one per minute over the totaltimespan illustrated on the horizontal axis 202. In this particularexample, the record covers a full 24-hour period, however embodiments ofthe invention may require only a portion of the full record 206,comprising a pre-sleep period 208, a sleep period 209, and a post-sleepperiod 210.

In some embodiments, the pre-sleep 208, sleep 209 and post-sleep 210periods may be automatically identified. One technique for automaticidentification of the sleep period 209 is through the use of an activitymonitor, such as an accelerometer which may be incorporated into theheart rate monitor 108, or into another wearable device worn by thesubject 106. FIG. 2(b) shows a graph 212 of subject activity obtainedusing such an activity monitor, and corresponding with the heartbeatrecord of FIG. 2(a). The horizontal axis 214 shows time, while thevertical axis 216 is an activity index, which is computed based upon thelevel of activity detected by the activity monitor during each minute ofthe recording period. The trace 218 of the activity record shows threevery distinct periods, i.e. a first waking period 220 of relatively highactivity, a sleep period 221 in which there is little or no activity,and a further waking period 222 of high activity.

The extremely distinct transitions between periods 220, 222, of highactivity, and period 221 of low activity, enables relatively simple andaccurate extraction of a sleep onset time 224 and a sleep conclusiontime 226, separating the pre-sleep 208, sleep 209, and post-sleep 210periods.

While activity levels provide one mechanism to identify the sleep onset224 and sleep conclusion 226 times, other methods may be used inalternative embodiments. For example, it is also apparent from the graph200 that the sleep period 209 corresponds with a general reduction inheart rate. Accordingly, suitable processing of the heartbeat record 206may be employed to assist in identifying the sleep onset 224 and sleepconclusion 226 times. Additionally, or alternatively, the subject 106may provide an estimate of sleep and waking times in order to assist inthe detection of sleep onset 224 and conclusion 226. It will thereforebe appreciated that various techniques to identify these transitiontimes with sufficient accuracy and reliability are available for use indifferent embodiments of the invention.

FIG. 3 is a flowchart 300 showing a method of assessing a mental state,e.g. mental health, of the subject 106, according to an embodiment ofthe invention. Firstly, at step 302, a heartbeat record of the subjectis received as input. In initial processing 304, the sleep period 209,having sleep onset time 224 and sleep conclusion time 226, isidentified.

The assessment method 300, which may be implemented via suitable programinstructions executed by the processor 114 of the assessment platform104, then proceeds to further analyse the heartbeat record in order toperform an assessment of the subject's stress levels. In order to dothis, information in the knowledge base is accessed 306. Exemplarycontents of the knowledge base are described below with reference toFIGS. 5(a) to 5(c), while corresponding exemplary training methods forconstructing the knowledge base are described with reference to FIGS.7(a) and 7(b). For present purposes it is sufficient to note that theinformation accessed in the knowledge base is based upon expertevaluation of a training set of subjects, and is constructed so as toenable the assessment platform 104 to estimate the mental state of thesubject 106 based upon the knowledge base contents. Generally, thisinvolves a process 308 of computing one or more metrics associated withthe mental state of the subject 106, and generating an indication of themental state based upon those metrics.

At step 310 a resulting indication of mental state, e.g. a mental healthindication, is output. The output result may be stored in a subjectrecord within the non-volatile storage 116, in the knowledge base 124,or in some other database. Alternatively, or additionally, the resultingindication may be presented to the subject and/or to a supervisinghealth care professional, for example via a web interface, or via anapplication interface, using software executing on a connected device,such as the smartphone 110 or desktop PC 112.

FIGS. 4(a) and 4(b) are flowcharts corresponding with two alternativecomputational models embodying the invention. FIGS. 5(a) to 5(c) areblock diagrams illustrating contents of the knowledge base for thesemodels.

According to a first model, herein termed the ‘template model’, aprocess of computing metrics and generating an indication of subjectmental state is represented by the flowchart 400, and the knowledge basecontents 500, 504. More particularly, the knowledge base 124 containscontent 500 which includes a ‘normal template’ 502. The normal template502 is a representative record corresponding with a patient without anysignificant mental health issues. The knowledge base 124 furthercontains content 504 which includes a ‘depression template’ 506. Thedepression template 506 is a representative record corresponding with asubject clinically diagnosed with depression. The way in which thenormal template 502 and the depression template 506 are obtained will bedescribed in greater detail below with reference to FIG. 7(a).

Returning to FIG. 4(a), in step 402 a metric is computed for theassessment subject 106, which comprises a measure of difference betweenthe heartbeat record of the subject, and the normal template 502. Atstep 404, a second metric is computed for the assessment subject 106,which comprises a measure of difference between the heartbeat record ofthe subject, and the depression template 506. In other embodiments ofthe invention, templates may be generated corresponding with othermental health conditions, such as anxiety, panic disorder, OCD,schizophrenia, and so forth. If such templates exist, similar measuresof difference are computed, comprising further metrics correspondingwith each template, as indicated by the ellipsis 406. A suitable measureof difference may be, for example, a mean squared difference between thesubject heartbeat record and the template in each case. The differencemay be computed over the entirety of the subject heartbeat record, orover only a selected portion of the heartbeat record. In particular, thedifference may be computed for the portion of the subject heartbeatrecord corresponding with the sleep period, i.e. between the sleep onsettime, and the sleep conclusion time.

At step 408, the mental state of the subject 106 is classified bycomparing the difference metrics computed at step 402 and step 404 (and,if available, any further difference metrics computed at steps 406),with the smallest value determining the indication of mental state ofthe subject 106.

The flowchart 410, and corresponding knowledge base content 508,exemplify a class of multi-parametric computational models. Themulti-parametric models described herein employ four metrics that arecomputed from the input heart rate record of the subject 106. These fourmetrics are:

-   -   the mean awake heart rate, i.e. the average heart rate during        the pre-sleep 208 and post-sleep 210 periods;    -   the ratio of heart rates, computed as a ratio between the        average waking heart rate, and the average heart rate during the        sleep period 209;    -   a first slope metric, being a measure of the slope (i.e. change        as a function of time) of the subject's heart rate during the        first half of the sleep period 209; and    -   a second slope metric, being a measure of the slope of the heart        rate in the second half of the sleep period 209.

As will be appreciated, these four parameters fully define apiecewise-linear representation of the patient heartbeat record, havinga constant waking heart rate value and a sleeping heart rate value thatchanges in accordance with the first slope metric during the first halfof the sleep period 209, and in accordance with the second slope metricduring the second half of the sleep period 209. The inventors have foundthis particular parameterisation of the heartbeat record to provide aneffective basis for machine learning and prediction of mental state,with the assistance of expert assessment of subjects in a training set.

Accordingly, at steps 412, 414, 416 and 418 the four metrics describedabove are computed.

According to an exemplary multi-parametric computational model, theknowledge base 124 contains content 508 which comprises one or more datastructures, e.g. 510, 512. In the presently disclosed embodiment, thesedata structures represent classification trees. A first classificationtree 510 is constructed to classify the subject 106, based upon the fourcomputed metrics discussed above, as ‘normal’ or ‘not normal’. A secondclassification tree 512 is constructed to classify the subject 106,based upon the four metrics, as ‘depressed’ or ‘not depressed’. The wayin which the classification trees 510, 512 are constructed will bedescribed in greater detail below with reference to FIG. 7(b).

Returning to FIG. 4(b), at step 420 the metrics computed for the subject106 are run through the first classification tree 510 at step 420. Theoutput is checked at step 422, and if the subject 106 is classified asnormal the process terminates at 424, with a corresponding ‘normal’indication. Otherwise, the second classification tree is run at step426. The output is checked at step 428, and if the subject 106 isclassified as depressed then the process terminates with an indicationof ‘depressed’ at 430. Otherwise, an indication of neither normal nordepressed is returned 432.

In all cases, the next steps, in terms of diagnosis and treatment of thesubject 106, will occur in conjunction with a health care practitioner.For example, a test in accordance with an embodiment of the inventionmay be ordered by the subject's local doctor or general practitioner. Ifthe resulting indication is ‘normal’, then the practitioner maydetermine that no further action is necessary, or may order furthertests of a similar or different nature. However, in the event that anindication of depression, or otherwise abnormal mental state, isobtained, then the practitioner may determine that some intervention isappropriate, such as treatment and/or referral to a specialist, such asa psychologist or psychiatrist, for further diagnosis and treatment.

Turning now to FIG. 6, there is shown a block diagram 600 illustratingthe main software processing components of a computer implementationembodying the invention. The input heartbeat record data 602 isprocessed by sleep detection module 604, in order to identify the sleeponset and conclusion times. The record is optionally further processedby a rescaling module 606. The rescaling module processes the input data602 in order to obtain a rescaled record, wherein the heart rate valueshave been normalised between zero and one, and the time adjusted to astandard scale, e.g. zero to 1,000 time units. Of the embodimentsdescribed in detail above, the rescaling is employed in the templatemodel, in which it is important to ensure similarity among all of theheartbeat records that are being compared against the normal template502, the depression template 506, and/or any other templates containedwithin the knowledge base 124. Rescaling is not required for themulti-parametric model described above, although it may be used in thecomputation of other metrics in accordance with alternative embodimentsof the invention.

Metric calculation module 608 computes the relevant metric, or metrics,associated with the particular computational model used in an embodimentof the invention. For example, in the template model the metriccalculation module 608 computes a first value representing thedifference between the heartbeat record of the subject 106 and thenormal template 502, and a second value representing the differencebetween the heartbeat record of the subject 106 and the depressiontemplate 506. In the multi-parametric models, the metric calculationmodule 608 computes the four metrics described above, with reference toFIG. 4(b).

In some embodiments, in order to compute the metric, or metrics, themetric calculation module 608 accesses the knowledge base 124. Forexample, in the template model, the metric calculation module 608retrieves the normal template 502 and the depression template 506 fromthe knowledge base 124.

The decision module 610 classifies the mental state of the subject 106according to the rules associated with the particular computationalmodel. For example, in the template model the decision module 610classifies the mental state of the subject 106 by comparing the firstand second distance values, corresponding with the normal and depressiontemplates, to determine which template is most similar to the heart beatpattern of the subject 106.

In the classification tree model, the decision module 610 classifies themental state of the subject 106 by executing the one or moreclassification trees stored in the knowledge base 124.

Typically, the decision module 610 requires access to the knowledge base124, in order to retrieve the decision criteria. An output mental stateindication 612 is produced from the decision module 610.

FIG. 7(a) shows a flowchart 700 corresponding with the algorithm forknowledge base construction according to the template model. For thisalgorithm, and for the multi-parametric classification tree algorithmdiscussed below with reference to FIG. 7(b), a precondition is that theknowledge base 124 includes a data set of training records. Eachtraining record comprises a heartbeat record of a test subject, alongwith an associated diagnosis/assessment performed by an expert, such asa trained medical practitioner. The assessment may be conducted basedupon the expert's review of the test subject heart rate records or maybe obtained by other diagnostic means, such as interviews between eachtest subject and the expert assessor. It is these actual assessmentsassociated with the data in the training set that provide the primaryexpert knowledge within the knowledge base. This information is thenused to build computational models embodying this expert knowledge,which can then be used to generate an indication of the possible mentalstate of a subsequent unseen subject 106, based upon an input heartbeatrecord of the subject.

Returning to the template model training algorithm 700, at step 702 afirst classification for training is set. This classification isselected from one of the available diagnoses performed by the expertclinician and associated with a subset of the training records in theknowledge base 124. Accordingly, for example, the first classificationselected at step 702 may be ‘normal’.

At step 704 all of the records from the training set having the firstclassification (e.g. ‘normal’) are retrieved. Each record comprises asequence of heartbeat data samples, such as those illustrated in thegraph of FIG. 2(a). At step 706 the retrieved data records are rescaled,such that heart rate is normalised between zero and one, and sleepperiod durations are normalised to a common timescale. At step 706, anaverage of all of the retrieved and rescaled test subject records iscomputed. This is a sample-by-sample averaging process, which results inthe generation of a single representative heartbeat record, i.e. thetemplate. In this example, the initial template is thus the normaltemplate 502, which is then stored in the knowledge base 124.

At step 710 a check is performed to determine whether there are furtherclassifications for which templates are required. In the exemplaryembodiment, at least one further template is generated, correspondingwith subjects within the training set who have been assessed assuffering from depression. Accordingly, at step 712 the classificationis set to ‘depression’, and the retrieval 704, rescaling 706, andtemplate computation 708 steps are repeated.

The process of computing templates can be continued for allclassifications for which expert clinician assessments or diagnosesexist within the training set.

FIG. 7(b) shows a flowchart 720 of a knowledge base construction methodcomprising construction of classification trees, and corresponding withthe computational model 410 shown in FIG. 4(b). At step 722, trainingdata records are retrieved from the knowledge base 124. The recordsretrieved at step 722 may comprise all of the records in the trainingset, or may comprise a selected subset.

At step 724, the set of four exemplary metrics (i.e. mean waking heartrate, heart rate ratio, first slope metric, and second slope metric) arecomputed for each one of the retrieved training data records.Accordingly, there is obtained from the training set a collection ofrecords of the form:(x;Y)=(x ₁ ,x ₂ ,x ₃ ,x ₄ ;Y)

In the above expression, the dependent variable Y represents the mentalhealth state of each subject in the training set, as assessed by theexpert clinician (e.g. ‘normal’, ‘depression’, etc), while the vector xis composed of the four metrics.

Given this data, at step 726 a first classification is selected, forexample ‘normal’. At step 728 the data records are partitioned such thateach record is classified as falling within the classification set (i.e.having an assessment of ‘normal’), or falling outside the classificationset (i.e. any assessment other than ‘normal’, generically being ‘notnormal’).

At step 730 a classification tree is constructed for distinguishingbetween ‘normal’ and ‘not normal’ within the training set, andaccordingly for predicting membership of these complementaryclassifications in future unseen data. Any suitable known decision treelearning algorithm may be employed at step 730, including: ID3; C4.5;CART; CHAID; MARS; and/or conditional inference trees. Existing softwaretools including, though not limited to, MATLAB and R, or existingprogramming libraries, such as scikit-learn for the Python programminglanguage, may be employed to implement the learning algorithm at step730.

At step 732, a check is performed to determine whether there are furtherclassifications for which classification trees must be generated. If so,then the next classification value is selected at step 734, and steps728 and 730 are repeated. In the exemplary embodiment, a secondclassification tree is generated for distinguishing between ‘depression’and ‘not depression’.

By way of illustration of the effectiveness of the classification treealgorithms employed in an embodiment of the invention, FIGS. 8 to 10show a number of three-dimensional chart representations forsegmentations of subjects in a training set between ‘normal’ and ‘notnormal’. Each chart has axis representing three of the four metrics,namely the heart rate ratio 800, the first slope metric 900 and thesecond slope metric 1000. The fourth metric, namely waking heart rate,is different for each of FIGS. 8, 9 and 10, i.e. each represents oneslice through the four-dimensional space defined by the four metrics. Inparticular, FIGS. 8(a) and 8(b) show segmentation of subjects between‘normal’ 802 and ‘not normal’ 804 respectively, for an average wakingheart rate of around 80 beats per minute. FIGS. 9(a) and 9(b) showsimilar segmentation 902, 904 for a waking heart rate of around 87.7beats per minute, while the segmentations 1002, 1004 in FIGS. 10(a) and10(b) are for an average waking heart rate of around 96 beats perminute.

It is clear from the three sets of charts in FIGS. 8, 9 and 10 that, foreach value of average waking heart rate, there is a distinctsegmentation between ‘normal’ and ‘not normal’ subjects. For example, ataround 80 beats per minute, ‘normal’ subjects are clustered within themetric space in two groups, forming a ‘galley’. Conversely, the ‘notnormal’ subjects are clustered within the metric space in a singlegrouping, corresponding with the ‘aisle’. At a heart rate of around 87.7beats per minute, the ‘normal’ subjects are distributed in the form of a‘table’ within metric space, while the ‘not normal’ subjects areclustered within an approximate cube shape. Finally, at around 96 beatsper minute, the ‘normal’ subjects are clustered in two connectingperpendicular planes, while again a ‘cube-like’ structure characterisesthe distribution of ‘not normal’ subjects within the metric space.

It can also be inferred from the ‘slices’ illustrated in FIGS. 8, 9 and10 that there is an evolution in the partitioning between ‘normal’ and‘not normal’ within the metric space as heart rate increases. The‘galley’ at around 80 beats per minute evolves into the ‘table’structure at around 87.7 beats per minute, while the ‘aisle’ structureof ‘not normals’ expands to occupy the space under the ‘table’. Thisevolution can be seen to continue as average working heart rateincreases from around 87.7 beats per minute to 96 beats per minute.

Some embodiments of the invention may be configured to provideadditional information in the form of an objective measure of a ‘degree’of the subject's state of mental health, e.g. a quantitative answer tothe question ‘how normal?’ (or ‘how depressed’?) Considering the normalcase, for example, a distance from the normal centroid may be evaluatedand used to provide further quantitative information. For a set ofmetrics m={m₁, m₂, . . . , m_(N)} of dimension N, a distance d from thenormal centroid m_(n)={m_(n,1), m_(n,2), . . . , m_(n,N)} is given by:

$d = {\sum\limits_{k = 1}^{N}{{m_{k} - m_{n,k}}}}$

The measure d is thus a quantitative indication of how far away thesubject is from the ‘average’ normal subject from the training set.

Turning now to FIG. 11, there is shown a process 1100 for evaluation,diagnosis and treatment employing an embodiment of the invention 104.The process 1100 involves a clinician 1102, such as a doctor/generalpractitioner, ordering a test of mental health of a patient using thesystem 104. At 1104 the patient undergoes heart rate measurement over asuitable period, including a sleeping period, and the data is collectedfor example via an app on a portable device 110 or an application orweb-based interface executing on a PC 112, and then uploaded to theserver 104 for analysis.

Once the analysis is complete, the results are stored within securestorage of the server 104, and are made accessible to the clinician 1102via a secure access interface 1106, such as a web portal. The clinician1102 is thereby able to review the results of the measurement andanalysis, and determine the appropriate next steps in diagnosis andtreatment of the patient. For example, if the results are ‘normal’, andyet the patient is exhibiting continuing adverse symptoms, the clinician1102 may determine that additional testing or other assessment isrequired. If the results indicate that the patient is depressed, theclinician may reach a corresponding diagnosis and/or may refer thepatient to a specialist, such as a psychologist or psychiatrist, forfurther assessment and treatment. If the assessment is that the patientis not depressed, but is also not normal, further testing and/orreferral may be indicated. These decisions remain in the hands of theclinician 1102, however the assessment performed by the server 104according to an embodiment of the invention clearly provides a useful,consistent and objective tool to assist the clinician 1102.

Furthermore, if treatment of the patient, for example by counselling ordrugs, is prescribed then the cycle of recording 1104, analysis by theserver 104, and review 1106 by the clinician 1102 may be repeated whiletreatment is ongoing. Such ongoing assessment provides a continuingobjective measurement of the effectiveness of treatment. If the assessedmental state of the patient improves, for example moving from‘depression’ to ‘normal’ indication, then the treatment may be regardedas successful. If, on the other hand, no objective positive change inthe indicated mental state of the patient is observed, the clinician1102, and/or any specialist to whom the patient may have been referred,may consider adjusting the treatment, for example by changing orsupplementing pharmaceutical or counselling options.

In summary, embodiments of the present invention provide methods andsystems enabling measurement, monitoring and assessment of mental state,and in particular indications of mental health of individual subjects,via simple and non-invasive heartbeat measurements. Advantageously,measurements may be performed using unobtrusive wearable devices,enabling subjects to go about their normal daily activities. Assessmentsare automatically generated using computational models, for exampleexecuted on a server accessible via the Internet, using a knowledge basecomprising expert assessment information.

Services and applications provided in accordance with embodiments of theinvention may be available to subjects individually, but may moreusefully be made available via health care professionals, such as apatient subject's own doctor. This enables the doctor to instruct thepatient in proper operation of the monitoring device, and proper conductof the heart measurements, and to receive the output indication of thepatient's state of mental health directly. Based on this indication, andother patient health information available to the doctor, professionalrecommendations may be made regarding the possible diagnosis andtreatment of any adverse mental health condition from which the patient.In appropriate cases a doctor may elect to refer a patient to aspecialist, such as a psychiatrist, for further assessment, testing,diagnosis and/or treatment.

The assessment platform 104 may keep historical records, and make theseavailable via the Internet, such that individuals and/or theirsupervising doctors can conduct ongoing monitoring of mental health.

Potential benefits of embodiments of the invention include improved andobjective identification of individuals who are suffering from, or atrisk of, mental health problems, such as depression. The non-invasiveand unobtrusive nature of the heart-rate measurements taken usingwearable devices ensures a low barrier to compliance, and may enableearly detection of potential issues, such that diagnosis and treatmentmay be undertaken prior to progression of a problem, thus reducingadverse outcomes and health care costs. Accordingly, numerous benefitsmay be obtained by individuals, healthcare professionals, and bysociety.

While particular embodiments have been described, by way of exampleonly, a person skilled in the relevant arts will appreciate that anumber of variations are possible, within the scope of the presentinvention. Accordingly, the exemplary embodiments should not be regardedas limiting, but rather the invention is as defined in the claimsappended hereto.

The claims defining the invention are as follows:
 1. Acomputer-implemented method of assessing a mental state of a subject,the method comprising: a) receiving, as input, a heartbeat record of thesubject, which comprises a sequence of heartbeat data samples obtainedover a time span which includes a pre-sleep period, a sleep periodhaving a sleep onset time and a sleep conclusion time, and a post-sleepperiod; b) identifying, within the heartbeat record, at least the sleeponset time and the sleep conclusion time; c) computing metrics of thesubject from the heartbeat record, the metrics including: i) a meanawake heart rate calculated as an average heart rate during thepre-sleep and post-sleep periods; ii) a ratio of a mean awake heart rateand a mean asleep heart rate; iii) a first slope metric indicative of achange over time of the subject's heart rate during a first half of thesleep period; and, iv) a second slope metric indicative of a change overtime of the subject's heart rate during a second half of the sleepperiod; d) accessing a knowledge base which comprises data obtained viaexpert evaluation of a training set of subjects and which embodies acomputational model of a relationship between mental state and saidmetrics, the computational model comprising data structures representingclassification trees obtained by applying a decision tree learningalgorithm over said metrics computed by processing heartbeat records ofa training set of subjects and the classification trees including: i) afirst classification tree data structure that classifies metricscomputed from the heartbeat record of the subject into ‘normal’ or ‘notnormal’; and ii) a second classification tree data structure thatclassifies the metrics computed from the heartbeat record of the subjectinto ‘depressed’ and ‘not depressed; e) applying the metrics of thesubject to the computational model to generate an indication of mentalstate by: i) classifying the subject as ‘normal’ or ‘not normal’ byexecuting the first classification tree; and ii) in the event that thesubject is classified as ‘not normal’, classifying the subject as‘depressed’ or ‘not depressed’ by executing the second classificationtree; and f) providing, as output, the indication of mental state. 2.The method of claim 1 wherein the method comprises: l) partitioning thetraining set of subjects as ‘normal’ and ‘not normal’; m) generating thefirst classification tree data structure using the ‘normal’ and ‘notnormal’ partitioning; n) partitioning the training set of subjects as‘depressed’ and ‘not depressed’; and, o) generating the secondclassification tree data structure using the ‘depressed’ and ‘notdepressed’ partitioning.
 3. The method of claim 1 wherein the methodcomprises automatically identifying the sleep period using a record ofactivity of the subject measured using an activity monitor.
 4. Themethod of claim 1 wherein the method comprises automatically identifyingthe sleep period by processing the heartbeat record.
 5. The method ofclaim 1 wherein the method comprises storing results of assessment ofthe mental state of the subject within secure storage of a server andmaking the results accessible to a clinician via a secure accessinterface.
 6. The method of claim 1 wherein the method comprisesrepeating the assessment while treatment is ongoing to provide acontinuing objective measurement of the effectiveness of treatment. 7.The method of claim 1 wherein the method comprises adjusting atreatment, and repeating the assessment to assess the effectiveness ofthe adjustment in treatment.
 8. A computer-implemented system forassessing a mental state of a subject, the system comprising: g) atleast one microprocessor; h) at least one non-volatile storage devicecontaining a knowledge base which comprises data obtained via expertevaluation of a training set of subjects and which embodies acomputational model of a relationship between mental state and metricscomputed from a heartbeat record, the computational model comprisingdata structures representing classification trees obtained by applying adecision tree learning algorithm over said metrics computed byprocessing heartbeat records of a training set of subjects and theclassification trees including: i) a first classification tree datastructure that classifies metrics computed from the heartbeat record ofthe subject into ‘normal’ or ‘not normal’; and ii) a secondclassification tree data structure that classifies the metrics computedfrom the heartbeat record of the subject into ‘depressed’ and ‘notdepressed; i) at least one computer-readable memory device operativelyassociated with the microprocessor; and j) an input/output interfaceoperatively associated with the microprocessor, k) wherein the memorydevice contains computer-executable instruction code which, whenexecuted via the microprocessor, causes the microprocessor to effect amethod comprising steps of: i) receiving, via the input/outputinterface, a heartbeat record of the subject, which comprises a sequenceof heartbeat data samples obtained over a timespan which includes apre-sleep period, a sleep period having a sleep onset time and a sleepconclusion time, and a post-sleep period; ii) identifying, within theheartbeat record, at least the sleep onset time and the sleep conclusiontime; iii) computing metrics of the subject from the heartbeat record,the metrics including: (1) a mean awake heart rate calculated as anaverage heart rate during the pre-sleep and post-sleep periods; (2) aratio of a mean awake heart rate and a mean asleep heart rate; (3) afirst slope metric indicative of a change over time of the subject'sheart rate during a first half of the sleep period; (4) a second slopemetric indicative of a change over time of the subject's heart rateduring a second half of the sleep period; iv) applying the metrics ofthe subject to the computational model to generate an indication ofmental state by: (1) classifying the subject as ‘normal’ or ‘not normal’by executing the first classification tree; and (2) in the event thatthe subject is classified as ‘not normal’, classifying the subject as‘depressed’ or ‘not depressed’ by executing the second classificationtree; and v) providing, via the input/output interface, the indicationof the mental state of the subject.
 9. The system of claim 8 wherein thesystem further comprises a heart rate monitor device configured to beworn by the subject during the timespan including the pre-sleep period,the sleep period and the post-sleep period.
 10. The system of claim 9wherein the heartbeat monitor comprises a communications interfaceconfigured for communication with a network-connected device.
 11. Thesystem of claim 10 wherein the input/output interface comprises anetwork interface providing access to a wide area network, and theheartbeat record is received via the wide area network from thenetwork-connected device of the subject.
 12. The system of claim 8wherein the microprocessor: p) partitioning the training set of subjectsas ‘normal’ and ‘not normal’; q) generating the first classificationtree data structure using the ‘normal’ and ‘not normal’ partitioning; r)partitioning the training set of subjects as ‘depressed’ and ‘notdepressed’; and, s) generating the second classification tree datastructure using the ‘depressed’ and ‘not depressed’ partitioning. 13.The system of claim 8 wherein the microprocessor automaticallyidentifies the sleep period using a record of activity of the subjectmeasured using an activity monitor.
 14. The system of claim 8 whereinthe microprocessor automatically identifies the sleep period byprocessing the heartbeat record.
 15. The system of claim 8 wherein thesystem includes a server having a secure store and wherein the systemstores results of the assessment of the mental state of the subjectwithin the secure storage of the server and makes the results accessibleto a clinician via a secure access interface.
 16. The system of claim 8wherein the microprocessor repeats the assessment while treatment isongoing to provide a continuing objective measurement of theeffectiveness of treatment.